package com.gis.aoitest.tableapi;

import com.gis.aoitest.beans.SensorReading;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Over;
import org.apache.flink.table.api.OverWindowedTable;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.Tumble;
import org.apache.flink.table.api.java.StreamTableEnvironment;
import org.apache.flink.types.Row;

/**
 * @author LnnuUser
 * @create 2021-09-10-下午10:38
 */
public class TableTest05_TimeAndWindow {

    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);

        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);


        // 2.1 读取文件
        String filePath = "/home/lnnu/IdeaProjects/FlinkTutorial/src/main/resources/sensor.txt";
        DataStreamSource<String> inputStream = env.readTextFile(filePath);

        // 3.转换为POJO
        DataStream<SensorReading> dataStream = inputStream.map(line -> {
            String[] fields = line.split(",");
            return new SensorReading(fields[0], new Long(fields[1]), new Double(fields[2]));
        } );

        // 4.将流转换成表，定义时间特性
        Table dataTable = tableEnv.fromDataStream(dataStream, "id, timestamp as ts, temperature as temp, rt.rowtime");

        tableEnv.createTemporaryView("sensor", dataTable);

        // 5.窗口操作
        // 5.1 Group Window
        // table API
        Table resultTable = dataTable.window(Tumble.over("10.seconds").on("rt").as("tw"))
                .groupBy("id, tw")
                .select("id, id.count, temp.avg, tw.end");

        // SQL
        Table sqlTable = tableEnv.sqlQuery("select id, count(id) as cnt, avg(temp) as avgTemp, tumble_end(rt, interval '10' second)" +
                " from sensor group by id, tumble(rt, interval '10' second)");

        // 5.2 Over window
        Table overResult = dataTable.window(Over.partitionBy("id").orderBy("rt").preceding("2.rows").as("ow"))
                .select("id, rt, id.count over ow, temp.avg over ow");

        // SQL
        Table overSqlResult = tableEnv.sqlQuery("select id, rt, count(id) over ow, avg(temp) over ow " +
                " from sensor " +
                " window ow as (partition by id order by rt rows between 2 preceding and current row)");


//        tableEnv.toAppendStream(dataTable, Row.class).print();
        dataTable.printSchema();
        tableEnv.toAppendStream(resultTable, Row.class).print("result");
        tableEnv.toRetractStream(sqlTable, Row.class).print("sql");

        tableEnv.toAppendStream(overResult, Row.class).print("result");
        tableEnv.toRetractStream(overSqlResult, Row.class).print("sql");

        env.execute();
    }
}
